In the context of primitive computer vision, it would be desired to have a relatively simple automatic approach capable of focusing its attention in the same way a human analyst would observing the same set of images. For reasons well known and highlighted in standard computer-vision books (see, for instance, Lagarias, J. C., J. A. Reeds, M. H. Wright, and P. E. Wright, “Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions,” SIAM Journal of Optimization, Vol. 9 Number 1, pp. 112-147, 1998), that expectation, however, is rarely met with experimental results, despite of the fact that sometimes the scenes in reference are characterized by image analysts as easy to focus their attentions to certain types of objects in the scenes.
Humans, of course, use a combination of knowledge-based, local and global information to aid in the analysis of a scene, a capability that may be reproduced by applying, for instance, layers of unsupervised learning methods complementing each other to perform this single task. For example, a suite of algorithms that includes an edge detector, an edge elongation, a clustering method, and a morphological size test might reproduce the humans' performance in certain conditions, albeit with a huge cost: computational time. Needless to say, the topic of automatic focus of attention (FOA) is an open and quite active area of research.
An advantage of choosing hyperspectral data over broadband is that a particular type of material, for instance, may be identified by testing a few pixels of the tested object, independently of the object's orientation, elevation angle, and distance from the sensor. Hyperspectral sensors are passive sensors that simultaneously record images for many contiguous and narrowly spaced regions of the electromagnetic spectrum. A data cube is created from these images, in which each image corresponds to the same ground scene and contains both spatial and spectral information about objects and backgrounds in the scene. These sensors employ several bands and have been used in various fields including urban planning, mapping, and military surveillance.
A further advantage of choosing anomaly detection over a particular type of material detection is that oftentimes the exact material of interest is not known a priori, or the number of spectra in a material of interest library is simply too exhaustive to search for all possible materials. The goal of an anomaly detector is to identify statistical outliers, i.e., data points that are atypical compared to the rest of the data. An anomaly detector that properly detects all, or a significant portion, of the pixels representing meaningful objects (targets) while at the same time having hundreds of meaningless detections (false alarms) has little practical value.
Most conventional anomaly detectors use multivariate models to define the spectral variability of the data, and the majority of the data pixels are assumed to be spectrally homogeneous and are modeled using a multivariate probability density function with a single set of parameters. Until now, no significant work had been done to find non-normal statistical models for the development of anomaly detection techniques using hyperspectral data. Conventional anomaly detectors may detect the presence of targets using hyperspectral data, but in the process they yield a large number of false alarms. This type of performance has little practical value.